@misc{Stojanov2024A,
  author = {Stojanov, Anaand Daniel, Ben Kei},
  title = {A decade of research into the application of big data and analytics in higher education: A systematic review of the literature},
  journal = {Education and Information Technologies},
  year = {2024},
  volume = {29},
  pages = {5807-5831},
  doi = {10.1007/s10639-023-12033-8},
  url = {https://doi.org/10.1007/s10639-023-12033-8},
  issn = {1573-7608},
  howpublished = {https://doi.org/10.1007/s10639-023-12033-8},
  note = {Education and Information Technologies},
  source = {Springer Link},
  abstract = {The need for data-driven decision-making primarily motivates interest in analysing Big Data in higher education. Although there has been considerable research on the value of Big Data in higher education, its application to address critical issues within the sector is still limited. This systematic review, conducted in December 2021 and encompassing 75 papers, analysed the applications of Big Data and analytics in higher education. The focus was on their usage in supporting learning, teaching and administration as reported in papers indexed in SCOPUS, Web of Science and IEEE Xplore. The key findings from the review revealed that Big Data and analytics are predominantly used to support learning and, to a lesser extent, guide teaching and informing administrative decision-making processes. The review also identified a set of studies focused on supporting student well-being. Further, we extend the use of Big Data in higher education to include the well-being of students and staff. This paper contributes to the growing debate on the practical use of Big Data and analytics to provide valuable insights for solving systemic challenges facing high education in the twenty-first century.},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8526388889},
  updated_at = {45544.6694907407},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {A decade of research into the application of big data and analytics in higher education: A systematic review of the literature},
  x_author = {Stojanov, Anaand Daniel, Ben Kei},
  x_year = {2024},
  x_doi = {10.1007/s10639-023-12033-8}
}
@misc{Fahd2023Designing,
  author = {Fahd, Kiranand Miah, Shah J.},
  title = {Designing and evaluating a big data analytics approach for predicting students' success factors},
  journal = {Journal of Big Data},
  year = {2023},
  volume = {10},
  pages = {159},
  doi = {10.1186/s40537-023-00835-z},
  url = {https://doi.org/10.1186/s40537-023-00835-z},
  issn = {2196-1115},
  howpublished = {https://doi.org/10.1186/s40537-023-00835-z},
  note = {Journal of Big Data},
  source = {Springer Link},
  abstract = {Reducing student attrition in tertiary education plays a significant role in the core mission and financial well-being of an educational institution. The availability of big data source from the Learning Management System (LMS) can be analysed to help with the attrition issues. This study aims to use an integrated Design Science Research (DSR) methodology to develop and evaluate a novel Big Data Analytical Solution (BDAS) as an educational decision support artefact. The BDAS as DSR artefact utilises Artificial Intelligence (AI) approaches to predict potential students at risk. Identifying students at risk helps to take timely intervention in the learning process to improve student academic progress for increasing their retention rate. To evaluate the performance of the predictive model, we compare the accuracy of the collection of representational AI algorithms in the literature. The study utilized an integrated DSR methodology founded on the similarities of DSR and design based research (DBR) to design and develop the proposed BDAS employing an specific evaluation framework that works on real data scenarios. The BDAS does not only aimto replace any existing practice but also support educators to implement a variety of pedagogical practices for improving students' academic performance.},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8522685185},
  updated_at = {45544.6683217593},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Designing and evaluating a big data analytics approach for predicting students' success factors},
  x_author = {Fahd, Kiranand Miah, Shah J.},
  x_year = {2023},
  x_doi = {10.1186/s40537-023-00835-z}
}
@misc{Zhang2022Quality,
  author = {Zhang, Ruihuaand Zhou, Jinchengand Hai, Taoand Zhang, Shixueand Iwendi, Marvellousand Biamba, Cresantusand Anumbe, Noble},
  title = {Quality assurance awareness in higher education in China: big data challenges},
  journal = {Journal of Cloud Computing},
  year = {2022},
  volume = {11},
  pages = {56},
  doi = {10.1186/s13677-022-00321-6},
  url = {https://doi.org/10.1186/s13677-022-00321-6},
  issn = {2192-113X},
  howpublished = {https://doi.org/10.1186/s13677-022-00321-6},
  note = {Journal of Cloud Computing},
  source = {Springer Link},
  abstract = {The quality assurance of higher education in China is an issue of vital international interest. To improve the international reputation of the nation's universities, steps must be taken to ensure a sustained focus on the quality assurance within its ranks. This paper is primarily focused on the quality assurance models operational in Chinese universities, the Big data challenges and the legal framework backing them. The paper also discusses the implementation of the models, the extent to which they meet international standards, and how they adhere to prevailing laws. The degree of success in attaining and maintaining quality and evaluation of quality improvement opportunities are also discussed. Some of the solutions recommended in this study are the participation of more teachers and students in quality management, more emphasis of Higher Education Institution (HEI) quality assurance on self-regulation and a learning-oriented approach and conducting sessions to collect anonymous feedback from students to reward staff with best practices. Some of the Quality Assurance practices/models adopted in Chinese Universities are the Ministry of Education (MOE) reviews; the Academic Degree Committee oversight; Higher Education Evaluation Center (HEEC) overview, University self-evaluation according to HEEC Indicators, and the Webometric Ranking Model.},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8496643519},
  updated_at = {45544.6670833333},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Quality assurance awareness in higher education in China: big data challenges},
  x_author = {Zhang, Ruihuaand Zhou, Jinchengand Hai, Taoand Zhang, Shixueand Iwendi, Marvellousand Biamba, Cresantusand Anumbe, Noble},
  x_year = {2022},
  x_doi = {10.1186/s13677-022-00321-6}
}
@misc{Flemisch2024Research,
  author = {Flemisch, Berndand Hermann, Sibylleand Herschel, Melanieand Pfl\{\\\"u\}ger, Dirkand Pleiss, J\{\\\"u\}rgenand Range, Janand Roy, Sarbaniand Takamoto, Makotoand Uekermann, Benjamin},
  title = {Research Data Management in Simulation Science: Infrastructure, Tools, and Applications},
  journal = {Datenbank-Spektrum},
  year = {2024},
  volume = {24},
  pages = {97-105},
  doi = {10.1007/s13222-024-00475-4},
  url = {https://doi.org/10.1007/s13222-024-00475-4},
  issn = {1610-1995},
  howpublished = {https://doi.org/10.1007/s13222-024-00475-4},
  note = {Datenbank-Spektrum},
  source = {Springer Link},
  abstract = {Research Data Management (RDM) has gained significant traction in recent years, being essential to allowing research data to be, e.g., findable, accessible, interoperable, and reproducible (FAIR), thereby fostering collaboration or accelerating scientific findings. We present solutions for RDM developed within the DFG-Funded Cluster of Excellence EXC2075 Data-Integrated Simulation Science (SimTech). After an introduction to the scientific context and challenges faced by simulation scientists, we outline the general data management infrastructure and present tools that address these challenges. Exemplary domain applications demonstrate the use and benefits of the proposed data management software solutions. These are complemented by additional measures for enablement and dissemination to foster the adoption of these techniques.},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8442013889},
  updated_at = {45544.6633449074},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Research Data Management in Simulation Science: Infrastructure, Tools, and Applications},
  x_author = {Flemisch, Berndand Hermann, Sibylleand Herschel, Melanieand Pfl\{\\\"u\}ger, Dirkand Pleiss, J\{\\\"u\}rgenand Range, Janand Roy, Sarbaniand Takamoto, Makotoand Uekermann, Benjamin},
  x_year = {2024},
  x_doi = {10.1007/s13222-024-00475-4}
}
@misc{Han2024Enhancing,
  author = {Han, Xiaoand Xiao, Shumeiand Sheng, Junand Zhang, Guangtao},
  title = {Enhancing Efficiency and Decision-Making in Higher Education Through Intelligent Commercial Integration: Leveraging Artificial Intelligence},
  journal = {Journal of the Knowledge Economy},
  year = {2024},
  doi = {10.1007/s13132-024-01868-2},
  url = {https://doi.org/10.1007/s13132-024-01868-2},
  issn = {1868-7873},
  howpublished = {https://doi.org/10.1007/s13132-024-01868-2},
  note = {Journal of the Knowledge Economy},
  source = {Springer Link},
  abstract = {The integration of artificial intelligence (AI) into financial management processes within academic institutions has ushered in a transformative era. This research paper delves into the profound implications of AI-driven financial integration, emphasizing its significance in aligning academic financial service functions with greater cohesion and efficiency. The establishment of intelligent financial systems, underpinned by meticulous data management and AI capabilities, has redefined the landscape of financial management in colleges and universities. This study explores the intricate relationship between AI-driven financial integration and its impact on job responsibilities and financial positions. It uncovers gaps in existing research and formulates pertinent questions to deepen our understanding of the integration process between industry and finance in the intelligent era. The research revolves around constructing a three-level financial management intelligent system, encompassing fine management, cost-effective operations, and enhancing links in financial processes. The findings underscore the transformative potential of AI in streamlining financial operations, emphasizing its role in liberating financial personnel from routine tasks. This paradigm shift not only streamlines financial operations but also augments financial productivity, unlocking the full potential of financial management within academic institutions. Theoretical implications highlight the need for ongoing theoretical development to accommodate the evolving role of AI in financial ecosystems. Managerial implications advocate for the strategic adoption of AI-driven financial platforms, fostering a culture of creativity and strategic contributions. Proactive managerial involvement in AI adoption can yield substantial benefits, requiring a nuanced approach to organizational change management and continuous innovation. This research paper paves the way for a more intelligent and integrated future in academic financial management, with AI driving efficiency and adaptability.},
  selection_criteria = {Estudios que no estén disponibles de forma gratuita o no sean accesibles a través de las bases de datos utilizadas.},
  created_at = {45542.8437615741},
  updated_at = {45543.088900463},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Rejected},
  x_title = {Enhancing Efficiency and Decision-Making in Higher Education Through Intelligent Commercial Integration: Leveraging Artificial Intelligence},
  x_author = {Han, Xiaoand Xiao, Shumeiand Sheng, Junand Zhang, Guangtao},
  x_year = {2024},
  x_doi = {10.1007/s13132-024-01868-2}
}
@misc{Ibrahim2022An,
  author = {Ibrahim, Walidand Ibrahim, Wissamand Zoubeidi, Taoufikand Marzouk, Sayedand Sweedan, Amrand Amer, Hoda},
  title = {An Online Management System for Streamlining and Enhancing the Quality of Learning Outcomes Assessment},
  journal = {Education and Information Technologies},
  year = {2022},
  volume = {27},
  pages = {11325-11353},
  doi = {10.1007/s10639-022-10918-8},
  url = {https://doi.org/10.1007/s10639-022-10918-8},
  issn = {1573-7608},
  howpublished = {https://doi.org/10.1007/s10639-022-10918-8},
  note = {Education and Information Technologies},
  source = {Springer Link},
  abstract = {Learning outcomes assessment is an effective academic quality assurance tool that enables educators to review and enhance the alignment between planned, delivered, and experienced curricula. Accurately assessing what students know and are able to do after completing a learning module is the first step to decide on the strategies to implement and the proper actions to take in order to ensure the continuous improvement of the student learning experience. Nonetheless, learning outcomes assessment processes in higher education are still facing major challenges that affect their proper and effective implementation. Hence, faculty do not usually experience noticeable improvement in the students' performance over several assessment cycles, which causes their frustration and reluctance to continue participating in the assessment process. This paper discusses the main issues that affect the implementation of the assessment process and prevent the closure of the assessment loop. It also introduces a unified assessment process and an online management system that have been developed recently to address the discussed issues. The online management system streamlines the assessment process, while providing administrators and quality assurance officers with valuable infographics and reports to effectively oversee the implementation of the assessment process. The system has been deployed at the United Arab Emirates University since fall 2018, and has been successfully used by faculty to assess the learning outcomes for more than 1000 courses each semester. Moreover, collected statistics showed that the online features provided by the system allowed faculty to continue their assessment tasks seamlessly during the COVID-19 pandemic.},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8535763889},
  updated_at = {45544.6736342593},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {An Online Management System for Streamlining and Enhancing the Quality of Learning Outcomes Assessment},
  x_author = {Ibrahim, Walidand Ibrahim, Wissamand Zoubeidi, Taoufikand Marzouk, Sayedand Sweedan, Amrand Amer, Hoda},
  x_year = {2022},
  x_doi = {10.1007/s10639-022-10918-8}
}
@misc{Rahmadian2023Digital,
  author = {Rahmadian, Ekoand Feitosa, Danieland Virantina, Yulia},
  title = {Digital twins, big data governance, and sustainable tourism},
  journal = {Ethics and Information Technology},
  year = {2023},
  volume = {25},
  pages = {61},
  doi = {10.1007/s10676-023-09730-w},
  url = {https://doi.org/10.1007/s10676-023-09730-w},
  issn = {1572-8439},
  howpublished = {https://doi.org/10.1007/s10676-023-09730-w},
  note = {Ethics and Information Technology},
  source = {Springer Link},
  abstract = {The rapid adoption of digital technologies has revolutionized business operations and introduced emerging concepts such as Digital Twin (DT) technology, which has the potential to predict system responses before they occur, making it an attractive option for smart and sustainable tourism. However, implementing DT software systems poses significant challenges, including compliance with regulations and effective communication among stakeholders, and concerns surrounding security, privacy, and trust with the use of big data. To address these challenges, this paper proposes a documentation framework for architectural decisions (DFAD) that applies the concept of big data governance to the digital system. The framework aims to ensure accountability, transparency, and trustworthiness while adhering to rules and regulations. To demonstrate its applicability, a case study and three case scenarios on the potential use of Mobile Positioning Data (MPD) in Indonesia for DT technology in smart and sustainable tourism were examined. The paper highlights the benefits of DFAD in shaping stakeholder communication and human--machine interactions while leveraging the potential of MPD to measure tourism statistics by Statistics Indonesia since 2016. Not only the documentation framework promotes compliance with regulations, but it also facilitates effective communication among stakeholders and enhances trust and transparency in the use of big data in DT technology for smart and sustainable tourism. This paper emphasizes the importance of effective big data governance and its potential to promote sustainable tourism practices. The multidisciplinarity approach on political science, software engineering, tourism, and official statistics provides an opportunity for academic contribution and decision-making processes.},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8529166667},
  updated_at = {45544.6708680556},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Digital twins, big data governance, and sustainable tourism},
  x_author = {Rahmadian, Ekoand Feitosa, Danieland Virantina, Yulia},
  x_year = {2023},
  x_doi = {10.1007/s10676-023-09730-w}
}
@misc{Kortian2024Challenges,
  author = {Kortian, Vikenand Pal, Souvikand Ghevondian, Nejhdehand Harrison, Norma},
  title = {Challenges and Issues in Implementing \{\\&\} Operationalising Big Data Analytics Capabilities in a major Australian Railway Organisation: A Case Study},
  journal = {SN Computer Science},
  year = {2024},
  volume = {5},
  pages = {639},
  doi = {10.1007/s42979-024-02953-8},
  url = {https://doi.org/10.1007/s42979-024-02953-8},
  issn = {2661-8907},
  howpublished = {https://doi.org/10.1007/s42979-024-02953-8},
  note = {SN Computer Science},
  source = {Springer Link},
  abstract = {This study examines the implementation of a Big Data Analytics (BDA) project within a major Australian freight and railway organisation. It also identifies the issues and challenges with data collection, data cleaning, data modelling, and data science software, and implements these models to deliver tangible business results. In addition, the project highlights the potential gains that a data analytics project, integrated with a data-driven culture, can provide through significant operational efficiencies and financial gains. Prior to 2019, the company had little exposure to Predictive Analytics. This study shows how the development of data science capability enables the creation of advanced predictive models, particularly in this case study, for the prediction of train wheel wear, and therefore a significant reduction in maintenance expenses Furthermore, a Data Analytics Maturity Assessment was conducted to determine the requirements to become a data-driven organisation. The outcome of the assessment was compared to recent global studies, and it was found that the organisation examined was significantly behind its counterparts in the areas of resources and analytic capabilities, and therefore required investment in these areas. Further studies to examine the degree of Data Analytics maturity within the Australian context are suggested. Organisations striving to become more data-driven need to plan and allocate resources for capability development in infrastructure, data management, employee quantitative skills, and governance.},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8532638889},
  updated_at = {45544.6722916667},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Challenges and Issues in Implementing \{\\&\} Operationalising Big Data Analytics Capabilities in a major Australian Railway Organisation: A Case Study},
  x_author = {Kortian, Vikenand Pal, Souvikand Ghevondian, Nejhdehand Harrison, Norma},
  x_year = {2024},
  x_doi = {10.1007/s42979-024-02953-8}
}
@misc{Miah2020Editorial,
  author = {Miah, Shah J.and Miah, Muhammedand Shen, Jun},
  title = {Editorial note: Learning management systems and big data technologies for higher education},
  journal = {Education and Information Technologies},
  year = {2020},
  volume = {25},
  pages = {725-730},
  doi = {10.1007/s10639-020-10129-z},
  url = {https://doi.org/10.1007/s10639-020-10129-z},
  issn = {1573-7608},
  howpublished = {https://doi.org/10.1007/s10639-020-10129-z},
  note = {Education and Information Technologies},
  source = {Springer Link},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8485416667},
  updated_at = {45544.6658449074},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Editorial note: Learning management systems and big data technologies for higher education},
  x_author = {Miah, Shah J.and Miah, Muhammedand Shen, Jun},
  x_year = {2020},
  x_doi = {10.1007/s10639-020-10129-z}
}
@misc{Chugh2023Implementing,
  author = {Chugh, Riteshand Turnbull, Darrenand Cowling, Michael A.and Vanderburg, Robertand Vanderburg, Michelle A.},
  title = {Implementing educational technology in Higher Education Institutions: A review of technologies, stakeholder perceptions, frameworks and metrics},
  journal = {Education and Information Technologies},
  year = {2023},
  volume = {28},
  pages = {16403-16429},
  doi = {10.1007/s10639-023-11846-x},
  url = {https://doi.org/10.1007/s10639-023-11846-x},
  issn = {1573-7608},
  howpublished = {https://doi.org/10.1007/s10639-023-11846-x},
  note = {Education and Information Technologies},
  source = {Springer Link},
  abstract = {In a world driven by constant change and innovation, Higher Education Institutions (HEIs) are undergoing a rapid transformation, often driven by external factors such as emerging technologies. One of the key drivers affecting the design and development of educational delivery mechanisms in HEIs is the fast pace of educational technology development which not only impacts an institution's technical capacity to infuse hardware and software solutions into existing learning infrastructure but also has implications for pedagogical practice, stakeholder acceptance of new technology, and HEI administrative structures. However, little is known about the implementation of contemporary educational technology in HEI environments, particularly as they relate to competing stakeholder perceptions of technology effectiveness in course delivery and knowledge acquisition. This review fills that gap by exploring the evidence and analyses of 46 empirical research studies focussing on technology implementation issues in a diverse range of institutional contexts, subject areas, technologies, and stakeholder profiles. This study found that the dynamic interplay of educational technology characteristics, stakeholder perceptions on the effectiveness of technology integration decisions, theoretical frameworks and models relevant to technology integration in pedagogical practices, and metrics to gauge post-implementation success are critical dimensions to creating viable pathways to effective educational technology implementation. To that end, this study proposes a framework to guide the development of sound implementation strategies that incorporates five dimensions: technology, stakeholder perceptions, academic discipline, success metrics, and theoretical frameworks. This study will benefit HEI decision-makers responsible for re-engineering complex course delivery systems to accommodate the infusion of new technologies and pedagogies in ways that will maximise their utility to students and faculty.},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8465972222},
  updated_at = {45544.6647453704},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Implementing educational technology in Higher Education Institutions: A review of technologies, stakeholder perceptions, frameworks and metrics},
  x_author = {Chugh, Riteshand Turnbull, Darrenand Cowling, Michael A.and Vanderburg, Robertand Vanderburg, Michelle A.},
  x_year = {2023},
  x_doi = {10.1007/s10639-023-11846-x}
}
